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Verificarlo: checking floating point accuracy through Monte Carlo Arithmetic (1509.01347v4)

Published 4 Sep 2015 in cs.MS and cs.NA

Abstract: Numerical accuracy of floating point computation is a well studied topic which has not made its way to the end-user in scientific computing. Yet, it has become a critical issue with the recent requirements for code modernization to harness new highly parallel hardware and perform higher resolution computation. To democratize numerical accuracy analysis, it is important to propose tools and methodologies to study large use cases in a reliable and automatic way. In this paper, we propose verificarlo, an extension to the LLVM compiler to automatically use Monte Carlo Arithmetic in a transparent way for the end-user. It supports all the major languages including C, C++, and Fortran. Unlike source-to-source approaches, our implementation captures the influence of compiler optimizations on the numerical accuracy. We illustrate how Monte Carlo Arithmetic using the verificarlo tool outperforms the existing approaches on various use cases and is a step toward automatic numerical analysis.

Citations (62)

Summary

  • The paper introduces Verificarlo, a tool that integrates Monte Carlo Arithmetic to quantify and address floating point errors.
  • It leverages LLVM IR post-optimization to simulate rounding errors and estimate significant digits with high fidelity.
  • Experimental validations across languages demonstrate its potential to enhance accuracy in high-performance scientific computations.

Verificarlo: Checking Floating Point Accuracy Through Monte Carlo Arithmetic

The paper "Verificarlo: Checking Floating Point Accuracy Through Monte Carlo Arithmetic," presents a novel tool aimed at addressing numerical accuracy in floating point computations by leveraging Monte Carlo Arithmetic (MCA). The authors propose Verificarlo, an extension of the LLVM compiler, as a robust mechanism to automatically analyze floating point computations' uncertainties. This development is poised to enhance the landscape of scientific computing, which increasingly relies on high-resolution simulations on modern highly parallel hardware architectures.

Overview of Monte Carlo Arithmetic

Monte Carlo Arithmetic is an approach designed to model and quantify rounding errors and catastrophic cancellations in floating point computations. It models each result of a floating point operation as a random variable, thereby turning program executions into trials in a Monte Carlo simulation. This method provides an empirical distribution of the results from which confidence intervals and numerical stability insights can be drawn. Key metrics like the number of significant digits in a result can be estimated by examining the magnitude of the relative standard deviation across multiple Monte Carlo trials.

Introduction of Verificarlo

Verificarlo is introduced as an LLVM-based tool that automates the process of implementing MCA for floating point computations. Crucially, it operates at the level of optimized Intermediate Representation (IR), which allows it to sidestep the limitations of other source-to-source transformation tools like CIL+MCALIB. By instrumenting the binaries post-optimization, Verificarlo captures the effects of compiler optimizations on numerical results, providing a more true-to-execution analysis than techniques that operate at the source level.

Achievements and Validation

Through a series of experimental case studies, Verificarlo has demonstrated its ability to provide insightful numerical accuracy analyses. The tool's capability to automatically instrument entire programs, spanning languages like C, C++, and Fortran, has been validated against traditional methods, including CIL+MCALIB and the CADNA library. For example, in a paper involving the compensated summation algorithm, Verificarlo alone appropriately recognized the impact of specific compiler optimization flags on the algorithm's precision, as the simulated arithmetic closely matched the theoretical error bounds.

With the resolving of ill-conditioned linear systems, and examinations involving unstable branching and counter management, Verificarlo reliably assessed the number of significant digits and identified instances of numerical noise. Notably, its ability to approach large-scale problems effectively positions it as a potentially invaluable tool in both development and production environments.

Implications and Future Work

The capacity of Verificarlo to instrument programs automatically and estimate numerical accuracy—including capturing the effect of compiler optimizations—opens avenues for rigorous verification and validation procedures. This aspect is especially critical in the era of exascale computing where simulation resolutions surpass traditional experimental validation measures.

Future developments could focus on optimizing MCA sample processing to reduce computational overheads, enhancing the tool's capabilities in numerical debugging, and broadening the scope of statistical analysis in post-treatment. The exploration of MCA applications in various numeric algorithm classes will further cement Verificarlo's role as a comprehensive solution for scientific code accuracy-checking needs.

In closing, Verificarlo stands as an innovative step towards democratizing access to numerical accuracy analysis tools, paving a path for enhanced reliability and robustness in scientific computing workflows. The ongoing development and potential collaboration with industrial partners could further enhance its adaptability and application in real-world problem-solving scenarios.